Hoax Detection News Using Naïve Bayes and Support Vector Machine Algorithm

Authors

  • Nur Elyta Febriyanty IKIP Budi Utomo, Indonesia
  • M. Amin Hariyadi Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia
  • Cahyo Crysdian Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia

DOI:

https://doi.org/10.25008/ijadis.v4i2.1306

Keywords:

Hoax News, Support Vector Machine, Naive Bayes, detection, Machine learning, Classification

Abstract

Websites and blogs are well-known as media for broadcasting news in various fields such as broadcasting news. The validity of news articles can be valid or fake. Fake news is also known as hoax news. The purpose of making hoax news is to persuade, manipulate, and influence news readers to do things that contradict or prevent correct action. This study proposes to experiment with the Support Vector Machine and Naïve Bayes classifications to detect hoax news in Indonesian. This study uses a dataset from public data, namely news between valid news and hoaxes. The system can classify online news in Indonesian with the term frequency feature the machine vector Support algorithm and naïve Bayes classification. While the evaluation model used is the Confusion Matrix. The results of the comparison of the two models as a Support Vector Machine have an accuracy rate of 75,5%, and Naive Bayes has an accuracy rate of 88%. Therefore, for the classification of hoax news, we recommend the Naive Bayes model because it has a better level of accuracy than the Support Vector Machine.

Downloads

Download data is not yet available.

Plum Analytics

   

Dimensions

            

References

M. Muttaqin, "Internet Usage Behavior of the ICT Young Workforce in the Border Region," Pekommas, vol. 4, no. 1, p. 11, Apr. 2019, doi: 10.30818/jpkm.2019.2040102. https://doi.org/10.30818/jpkm.2019.2040102

C. Juditha, "Interaksi Komunikasi Hoax di Media Sosial serta Antisipasinya Hoax Communication Interactivity in Social Media and Anticipation," vol. 3, no. 1.

F. Prasetya and F. Ferdiansyah, "Analisis Data Mining Klasifikasi Berita Hoax COVID 19 Menggunakan Algoritma Naive Bayes," json, vol. 4, no. 1, p. 132, Sep. 2022, doi: 10.30865/json.v4i1.4852. https://doi.org/10.30865/json.v4i1.4852

T. W. S. Nagel, "Measuring fake news acumen using a news media literacy instrument," JMLE, vol. 14, no. 1, pp. 29-42, May 2022, doi: 10.23860/JMLE-2022-14-1-3. https://doi.org/10.23860/JMLE-2022-14-1-3

E. Elsaeed, O. Ouda, M. M. Elmogy, A. Atwan, and E. El-Daydamony, "Detecting Fake News in Social Media Using Voting Classifier," IEEE Access, vol. 9, pp. 161909-161925, 2021, doi: 10.1109/ACCESS.2021.3132022. https://doi.org/10.1109/ACCESS.2021.3132022

"Book Bhala-Keskar 2020.pdf."

"2020-Semi-Automatic_Classification_and_Duplicate_Detection_From_Human_Loss_News_Corpus.pdf."

Venkatesh and K. V. Ranjitha, "Classification and Optimization Scheme for Text Data using Machine Learning Naïve Bayes Classifier," in 2018 IEEE World Symposium on Communication Engineering (WSCE), Singapore, Singapore: IEEE, Dec. 2018, pp. 33-36. doi: 10.1109/WSCE.2018.8690536. https://doi.org/10.1109/WSCE.2018.8690536

A. Dey, R. Z. Rafi, S. Hasan Parash, S. K. Arko, and A. Chakrabarty, "Fake News Pattern Recognition using Linguistic Analysis," in 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan: IEEE, Jun. 2018, pp. 305-309. doi: 10.1109/ICIEV.2018.8641018. https://doi.org/10.1109/ICIEV.2018.8641018

"gilda2017-Evaluating Machine Learning Algorithms for.pdf."

Z. Li, W. Shang, and M. Yan, "News text classification model based on topic model," in 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan: IEEE, Jun. 2016, pp. 1-5. doi: 10.1109/ICIS.2016.7550929. https://doi.org/10.1109/ICIS.2016.7550929

"2019-A_Hybrid_Bidirectional_Recurrent_Convolutional_Neural_Network_Attention-Based_Model_for_Text_Classification.pdf."

A. Dhar, N. Dash, and K. Roy, "Classification of text documents through distance measurement: An experiment with multi-domain Bangla text documents," in 2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall), Dehradun, India: IEEE, Sep. 2017, pp. 1-6. doi: 10.1109/ICACCAF.2017.8344721. https://doi.org/10.1109/ICACCAF.2017.8344721

R. R. Sani, Y. A. Pratiwi, S. Winarno, E. D. Udayanti, and F. A. Zami, "Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Hoax pada Berita Online Indonesia," Jurnal Masyarakat Informatika, vol. 13, no. 2, 2022. https://doi.org/10.14710/jmasif.13.2.47983

S. Raza and C. Ding, "Fake news detection based on news content and social contexts: a transformer-based approach," Int J Data Sci Anal, vol. 13, no. 4, pp. 335-362, May 2022, doi: 10.1007/s41060-021-00302-z. https://doi.org/10.1007/s41060-021-00302-z

Downloads

Published

2023-10-06

How to Cite

Febriyanty, N. E., Hariyadi, M. A., & Crysdian, C. (2023). Hoax Detection News Using Naïve Bayes and Support Vector Machine Algorithm. International Journal of Advances in Data and Information Systems, 4(2), 191-200. https://doi.org/10.25008/ijadis.v4i2.1306
Abstract views : 207 times